{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3a44c1c",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from toolz import curry\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "import cvxpy as cp\n",
    "\n",
    "import toolz as f\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from matplotlib import style\n",
    "style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db1c219a",
   "metadata": {},
   "source": [
    "# Conformal Inference for Synthetic Controls\n",
    " \n",
    "## Synthetic Control Refresher\n",
    " \n",
    "Synthetic Control (SC) is a particularly useful causal inference technique for when you have a single treatment unit and very few control units, but you have repeated observation of each unit through time (although there are plenty of SC extensions in the Big Data world). The canonical use case is when you want to know the impact of the treatment in one geography (like a state) and you use the other untreated states as controls. In our Synthetic Control chapter, we've motivated the technique by trying to estimate the effect of Proposition 99 (a bill passed in 1988 that increased cigarette tax in California) in cigarette sales. \n",
    " \n",
    "In order to do that, we have to estimate what would have happened to California, had it not passed Proposition 99. This boils down to estimating the counterfactual $Y_{t}(0)$ so that we can compare it to the observed outcome in the post intervention periods:\n",
    " \n",
    "$$\n",
    "ATT = Y_{t}(1) - Y_{t}(0) = Y_{t} - Y_{t}(0)  \\text{ for } t \\geq 1988\n",
    "$$\n",
    " \n",
    "There are many methods to do that, among which, we have Synthetic Controls. Synthetic Controls tries to model $Y(0)$ for the treated unit by combining multiple control units in such a way that they mimic the pre-treatment behavior of the treated unit. In our case, this means finding a combination of states that, together,  approximate the cigarette sales trend in California prior to Proposition 99. This is done because we rarely have a control unit that follows the same pattern as the treatment unit. We can see that by plotting the cigarette sales trend for multiple states. Notice none of them have a trend that closely resembles that of California. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1031a79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 39)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/smoking.csv\")\n",
    "\n",
    "data = data.pivot(\"year\", \"state\", \"cigsale\")\n",
    "data = data.rename(columns={c: f\"state_{c}\" for c in data.columns}).rename(columns={\"state_3\": \"california\"})\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7d9c880",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cigarette Sales')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(data.drop(columns=[\"california\"]), color=\"C1\", alpha=0.5)\n",
    "plt.plot(data[\"california\"], color=\"C0\", label=\"California\")\n",
    "plt.vlines(x=1988, ymin=40, ymax=300, linestyle=\":\", lw=2, label=\"Proposition 99\", color=\"black\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Cigarette Sales\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301eb863",
   "metadata": {},
   "source": [
    "That is why we combine multiple treated units. The goal is, if we don't have a good enough control, we can craft a synthetic one that resembles the treated unit the way we want. \n",
    " \n",
    "In order to find the combination of states that better approximate the pretreatment trend of California, the Synthetic Control method runs a horizontal regression, where the rows are the time periods and the columns are the states. It tries to find the weights that, when multiplied by the control states, better approximate the treated state\n",
    " \n",
    "![img](data/img/synth-control/regr_space.png)\n",
    " \n",
    "Since we have more states (39, some were discarded from the analysis) than time periods, an unconstrained regression would simply overfit, which is why Synthetic Control imposes two restrictions:\n",
    " \n",
    "1. Weights must sum to 1;\n",
    "2. Weights must be non-negative;\n",
    " \n",
    "Or, in mathematical terms, let $\\pmb{y}$ be the vector of outcomes for the treated state in the pre-treated periods, $\\pmb{X}$ the $J$ by $T0$ matrix, where each column is a state $j$ and each row is a period $t$ prior to the intervention period, $T1 = T0 + 1$\n",
    " \n",
    "$$\n",
    "\\underset{w}{\\mathrm{argmin}} \\ ||\\pmb{y} - \\pmb{X} \\pmb{w}|| \\\\\n",
    "\\text{s.t } \\ \\sum w_j = 1 \\text{ and } \\ w_j > 0 \\ \\forall \\ j\n",
    "$$\n",
    " \n",
    "Combined, these constraints means we are defining the synthetic control as a convex combination of the control units. It also means we are not doing any dangerous extrapolation and that our synthetic control will use only a small subset of control units. \n",
    " \n",
    "![img](data/img/synth-control/extrapolation.png)\n",
    " \n",
    "Here is what this looks like in code, as an Sklearn estimator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f844ea6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, RegressorMixin\n",
    "from sklearn.utils.validation import check_X_y, check_array, check_is_fitted\n",
    "import cvxpy as cp\n",
    "\n",
    "class SyntheticControl(BaseEstimator, RegressorMixin):\n",
    "\n",
    "    def __init__(self,):\n",
    "        pass\n",
    "\n",
    "    def fit(self, X, y):\n",
    "\n",
    "        X, y = check_X_y(X, y)\n",
    "    \n",
    "        w = cp.Variable(X.shape[1])\n",
    "        objective = cp.Minimize(cp.sum_squares(X@w - y))\n",
    "        \n",
    "        constraints = [cp.sum(w) == 1, w >= 0]\n",
    "        \n",
    "        problem = cp.Problem(objective, constraints)\n",
    "        problem.solve(verbose=False)\n",
    "        \n",
    "        self.X_ = X\n",
    "        self.y_ = y\n",
    "        self.w_ = w.value\n",
    "        \n",
    "        self.is_fitted_ = True\n",
    "        return self\n",
    "        \n",
    "        \n",
    "    def predict(self, X):\n",
    "\n",
    "        check_is_fitted(self)\n",
    "        X = check_array(X)\n",
    "        \n",
    "        return X @ self.w_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10486ef3",
   "metadata": {},
   "source": [
    "Let's apply this method to our data, fitting it in the pre-intervention period (prior to 1988)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fb88349",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SyntheticControl()\n",
    "\n",
    "train = data[data.index < 1988]\n",
    "\n",
    "model.fit(train.drop(columns=[\"california\"]), train[\"california\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ed1debf",
   "metadata": {},
   "source": [
    "We can now plot, side by side the trend for California and for the synthetic control we've just created. The difference between these two lines is the estimated effect of Proposition 99 in California."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24356e7d",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[\"california\"], label=\"California\")\n",
    "plt.plot(data[\"california\"].index, model.predict(data.drop(columns=[\"california\"])), label=\"SC\")\n",
    "plt.vlines(x=1988, ymin=40, ymax=120, linestyle=\":\", lw=2, label=\"Proposition 99\", color=\"black\")\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba6fe3e",
   "metadata": {},
   "source": [
    "From the look of this plot, it looks like Proposition 99 had a pretty big effect on the reduction of cigarette sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18ecc017",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_data = data.assign(**{\"residuals\": data[\"california\"] - model.predict(data.drop(columns=[\"california\"]))})\n",
    "\n",
    "plt.plot(pred_data[\"california\"].index, pred_data[\"residuals\"], label=\"Estimated Effect\")\n",
    "plt.hlines(y=0, xmin=1970, xmax=2000, lw=2, color=\"Black\")\n",
    "plt.vlines(x=1988, ymin=5, ymax=-25, linestyle=\":\", lw=2, label=\"Proposition 99\", color=\"Black\")\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "443c8f52",
   "metadata": {},
   "source": [
    "## Inference for Grown Ups\n",
    " \n",
    "In the Synthetic Control chapter, we showed an inference procedure where we've permuted units, pretending control units where treated. This is also referred to as a placebo test, where we check the effect of units that haven't gone through the treatment. If the estimated effect in the treated unit is bigger than most of the placebo effects, we say that this effect estimate is significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fd96dac0",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "for state in data.columns:\n",
    "    \n",
    "    model_ier = SyntheticControl()\n",
    "    train_iter = data[data.index < 1988]\n",
    "    model_ier.fit(train_iter.drop(columns=[state]), train_iter[state])\n",
    "    \n",
    "    effect = data[state] - model_ier.predict(data.drop(columns=[state]))\n",
    "    \n",
    "    is_california = state == \"california\"\n",
    "    \n",
    "    plt.plot(effect,\n",
    "             color=\"C0\" if is_california else \"C1\",\n",
    "             alpha=1 if is_california else 0.5,\n",
    "             label=\"California\" if is_california else None)\n",
    "\n",
    "plt.hlines(y=0, xmin=1970, xmax=2000, lw=2, color=\"Black\")\n",
    "plt.vlines(x=1988, ymin=-50, ymax=100, linestyle=\":\", lw=2, label=\"Proposition 99\", color=\"Black\")\n",
    "plt.ylabel(\"Effect Estimate\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48711fd5",
   "metadata": {},
   "source": [
    "In our example, we can see that the post-treatment difference for California is quite extreme, when compared to the other states. However, there are also some states with terrible pre-treatment fit, which then translates to a huge error in the post-intervention period. The guideline here is to remove units with high pretreatment error, but how high is a bit more complicated. Not only that, this procedure assumes a random assignment of the intervention, which is hard to believe for this kind of policy intervention (see Abadie, 2021)\n",
    " \n",
    "One alternative method for inference is to recast the problem of effect estimation as counterfactual prediction. If you think about it, all we are trying to do is predict the counterfactual $Y_{i, t}(0)$ where $i$ is the treated unit and $t \\geq T1$, that is, in the post intervention period. If we do that, we can leverage the literature on **Conformal Prediction for inference**. Interestingly enough, this method is quite general and applies to other models of $Y_{i, t}(0)$ but let's focus just on Synthetic Controls here.\n",
    " \n",
    "To understand this procedure, let's first look at how we would do Hypothesis Tests and get P-Values.\n",
    "\n",
    "### Hypothesis Test and P-Values\n",
    " \n",
    "Let's say we are interested in testing the Hypothesis about the trajectory of effects in the post treatment period $\\theta = (\\theta_{T0+1}, ..., \\theta_{T})$\n",
    " \n",
    "$$\n",
    "H_0 : \\theta = \\theta^0\n",
    "$$\n",
    " \n",
    "For instance, if we wish to test for no effect whatsoever, we can set $\\theta^0 = (0, ..., 0)$. Notice that this hypothesis fully determines the counterfactual outcome in the absence of treatment:\n",
    " \n",
    "$$\n",
    "Y_t(0) = Y_t(1) - \\theta_t = Y_t - \\theta_t\n",
    "$$\n",
    " \n",
    "The key idea is to then generate data following the null hypothesis we want to test and check the residuals of a model for $Y(0)$ in this generated data. If the residuals are too extreme, we say that the data is unlikely to have come from the null hypothesis we've postulated. If this whole procedure sounds obscure at first, don't worry, it will become clear as we walk through a step by step implementation of it. \n",
    " \n",
    "The first step is to generate data under the null hypothesis. This is achieved by simply subtracting the postulated null from the outcome of the treated unit, just like in the equation above. Here is the code to do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1be1ab97",
   "metadata": {},
   "outputs": [],
   "source": [
    "def with_effect(df, state, null_hypothesis, start_at, window):\n",
    "    window_mask = (df.index >= start_at) & (df.index < (start_at +window))\n",
    "    \n",
    "    y = np.where(window_mask, df[state] - null_hypothesis, df[state])\n",
    "    \n",
    "    return df.assign(**{state: y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7f30c5f0",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(with_effect(data, \"california\", 0, 1988, 2000-1988+1)[\"california\"], label=\"H0: 0\")\n",
    "plt.plot(with_effect(data, \"california\", -4, 1988, 2000-1988+1)[\"california\"], label=\"H0: -4\")\n",
    "\n",
    "plt.ylabel(\"Y0 Under the Null\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afe8f41",
   "metadata": {},
   "source": [
    "If we postulate the null of no effect, the data under that null means that $Y(0) = Y(1) = Y$, which is just the trajectory of observed outcome we see for the treated state of California. Now, if we postulate that the null is -4, that is, Proposition 99 decreases cigarette sales by 4 packs, then $Y(0) = Y(1) - (-4)$, which shifts the trajectory of the post treatment outcomes by +4. This is very intuitive. If we think the bill decreases cigarette sales, then, in the absence of it, we should see higher levels of cigarette sales than the one we have in our observed data. \n",
    " \n",
    "The next part of the inference procedure is to fit a model for the counterfactual $Y(0)$ (which we get with the function we just created) in the entire data, pre **and** post-treatment period. This is an important distinction between how we usually fit synthetic controls. The idea here is that the model must be estimated with the entire data, under the postulated null hypothesis, to avoid huge post intervention residuals. With this model, we then compute the residuals $\\hat{u_t} = Y_t - \\hat{Y}_t(0)$ for all time periods $t$.\n",
    " \n",
    "The function to do that first uses the `with_effect` function we created earlier to generate data under then null. Then, it fits the model in this data under the null. Next, we estimate $Y(0)$ by making predictions with the recently fit model. Finally, we compute the residuals $\\hat{u}_t$ and stores everything in a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "73fe7272",
   "metadata": {},
   "outputs": [],
   "source": [
    "@curry\n",
    "def residuals(df, state, null, intervention_start, window, model):\n",
    "    \n",
    "    null_data = with_effect(df, state, null, intervention_start, window)\n",
    "            \n",
    "    model.fit(null_data.drop(columns=[state]), null_data[state])\n",
    "    \n",
    "    y0_est = pd.Series(model.predict(null_data.drop(columns=[state])), index=null_data.index)\n",
    "    \n",
    "    residuals = null_data[state] - y0_est\n",
    "    \n",
    "    test_mask = (null_data.index >= intervention_start) & (null_data.index < (intervention_start + window))\n",
    "    \n",
    "    return pd.DataFrame({\n",
    "        \"y0\": null_data[state],\n",
    "        \"y0_est\": y0_est,\n",
    "        \"residuals\": residuals,\n",
    "        \"post_intervention\": test_mask\n",
    "    })[lambda d: d.index < (intervention_start + window)]  # just discard  points after the defined effect window"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf743c46",
   "metadata": {},
   "source": [
    "With our data, to get the residuals for $H_0 : 0$, meaning Proposition 99 had no effect, we can simply pass 0 as the null for our function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "168809a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y0</th>\n",
       "      <th>y0_est</th>\n",
       "      <th>residuals</th>\n",
       "      <th>post_intervention</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>123.000000</td>\n",
       "      <td>112.529475</td>\n",
       "      <td>10.470525</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>121.000000</td>\n",
       "      <td>114.315723</td>\n",
       "      <td>6.684277</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>123.500000</td>\n",
       "      <td>119.302289</td>\n",
       "      <td>4.197711</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>124.400002</td>\n",
       "      <td>121.265554</td>\n",
       "      <td>3.134447</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>126.699997</td>\n",
       "      <td>124.356696</td>\n",
       "      <td>2.343301</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              y0      y0_est  residuals  post_intervention\n",
       "year                                                      \n",
       "1970  123.000000  112.529475  10.470525              False\n",
       "1971  121.000000  114.315723   6.684277              False\n",
       "1972  123.500000  119.302289   4.197711              False\n",
       "1973  124.400002  121.265554   3.134447              False\n",
       "1974  126.699997  124.356696   2.343301              False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SyntheticControl()\n",
    "\n",
    "residuals_df = residuals(data,\n",
    "                         \"california\",\n",
    "                         null=0.0,\n",
    "                         intervention_start=1988,\n",
    "                         window=2000-1988+1,\n",
    "                         model=model)\n",
    "\n",
    "residuals_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d3aade",
   "metadata": {},
   "source": [
    "The result is a dataframe containing the estimated residuals for each time period, something we will use going forward. Remember that the idea here is to see if that residual, in the post intervention period, is too high. If it is, the data is unlikely to have come from this null, where the effect is zero. To get a visual idea of what we are talking about, we can inspect the error of our model in the post intervention period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "62cffb85",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 4))\n",
    "residuals_df[[\"y0\", \"y0_est\"]].plot(ax=ax1)\n",
    "ax1.set_title(\"Y0 under H0: 0\");\n",
    "residuals_df[[\"residuals\"]].plot(ax=ax2);\n",
    "ax2.set_title(\"Residuals under H0: 0\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc2bf17",
   "metadata": {},
   "source": [
    "We can already see that the model fitted under $H_0: 0$ yields quite large and negative residuals, which is some evidence we might want to reject this null of no effect. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b310ad04",
   "metadata": {},
   "source": [
    "#### Test Statistic\n",
    " \n",
    "This visual evidence is interesting for our own understanding, but we need to be a bit more precise here. This is done by the definition of a **Test Statistic S**, which summarizes how big are the residuals and hence, how unikly is the data we saw, under the null. \n",
    " \n",
    "$$\n",
    "S(\\hat{u})_q = \\bigg(\\sum_{t=T0 + 1}^{T} |u_t|^q \\bigg) ^{1/q}\n",
    "$$\n",
    " \n",
    "Here, we focus on $q=1$, which gives us $S(\\hat{u}) = \\sum_{t=T0 + 1}^{T} |u_t|$.\n",
    " \n",
    "Notice that this statistic is computed using only the post-intervention period, with $t \\geq T0 + 1$. So, although we use all the data to fit our model for the counterfactual $Y(0)$, we check the residuals only for the outcome which concerns the formulated null hypothesis, that is, the post-intervention period. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7eda113d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_statistic(u_hat, q=1, axis=0):\n",
    "    return (np.abs(u_hat) ** q).mean(axis=axis) ** (1/q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e9d58789",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0:0  12.602929955114083\n"
     ]
    }
   ],
   "source": [
    "print(\"H0:0 \", test_statistic(residuals_df.query(\"post_intervention\")[\"residuals\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a4896c6",
   "metadata": {},
   "source": [
    "High values of this test statistic indicate poor post intervention fit and, hence rejection of the null. However, we could have pretty big test statistics in the post-intervention period if our model is poorly fitted, even if $H_0$ is true. This means we can't define high in absolute terms. Rather, we have to think about how high are the post intervention residuals - and test statistics - in comparison to the pre-intervention residuals. \n",
    " \n",
    "#### P-Value\n",
    " \n",
    "To compute the P-value, we block-permute the residuals, calculating the test statistic in each permutation. This procedure is better understood by the following picture\n",
    " \n",
    "![img](data/img/sc-conformal-inf/block-perm.png)\n",
    " \n",
    "Once we do that, we will end up with $T$ test statistics, one for each of the block permutations.\n",
    " \n",
    "Let $\\Pi$ be the set of all block permutations, by the definition of P-value \n",
    " \n",
    "$$\n",
    "\\text{P-value} = \\frac{1}{|\\Pi|}\\sum_{\\pi \\in \\Pi} \\mathcal{1}\\{S(\\hat{u}_{\\pi_0}) \\leq S(\\hat{u}_{\\pi})\\}\n",
    "$$\n",
    " \n",
    "and $\\hat{u}_{\\pi_0}$ is the original (unpermuted) vector or residuals. In plain terms, we are simply finding the proportion of times that the unpermuted test statistic is higher (more extreme) than the test statistics obtained by all possible block permutations. \n",
    " \n",
    "To implement this, we will make use of the `np.roll` function, which takes an array and circles it, mujustch like we've represented in the image above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2b3556db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_value(resid_df, q=1):\n",
    "    \n",
    "    u = resid_df[\"residuals\"].values\n",
    "    post_intervention = resid_df[\"post_intervention\"].values\n",
    "    \n",
    "    block_permutations = np.stack([np.roll(u, permutation, axis=0)[post_intervention]\n",
    "                                   for permutation in range(len(u))])\n",
    "    \n",
    "    statistics = test_statistic(block_permutations, q=1, axis=1)\n",
    "    \n",
    "    p_val = np.mean(statistics >= statistics[0])\n",
    "\n",
    "    return p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfa67f7b",
   "metadata": {},
   "source": [
    "We can now compute the P-value for $H_0: 0$. As we can see, it is a low P-value, but not extremely low. At $\\alpha=0.1$, we would not reject this null of no effect. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8cda9884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16129032258064516"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value(residuals_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f016994",
   "metadata": {},
   "source": [
    "Remember, this is the P-value for the null hypothesis which states that the effect in all time periods is zero: $\\theta = (\\theta_{T0+1}=0, ..., \\theta_{T}=0)$. From our effect plot from the Synthetic Control, we get the feeling that the effect of Proposition 99 is not a fixed number. We can see that it starts small, around -5, but gradually increases to -25. For this reason, it might be interesting to plot the confidence interval for effect each post treatment period individually, rather than just testing a null hypothesis about an entire affect trajectory."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5048e75",
   "metadata": {},
   "source": [
    "### Confidence Intervals\n",
    " \n",
    "To understand how we can place a confidence interval around the effect of each post-treatment period, let's first try to understand how we would define the confidence interval for a single time period. If we have a single period, then $H_0$ is defined in terms of a scalar value, rather than a trajectory vector $\\theta$. This means we can generate a fine line of $H_0s$ and compute the P-value associated with each null. For example, Let's say we think the effect of Proposition 99 in the year 1988 (the year it passed) is somewhere between -20 and 20. We can then build a table containing a bunch of $H_0$, from -20 to 20, and each associated P-value:\n",
    " \n",
    "```\n",
    "P-value(H_0: -20) = 0.01\n",
    "P-value(H_0: -19) = 0.01\n",
    "P-value(H_0: -18) = 0.02\n",
    "...\n",
    "P-value(H_0: 18) = 0.03\n",
    "P-value(H_0: 19) = 0.03\n",
    "P-value(H_0: 20) = 0.02\n",
    "```\n",
    " \n",
    "With the functions we've defined, this can be achieved by first appending the period of interest (1988 in this example) at the end of the pre-intervention period, creating what is called an augmented dataset. Then, we iterate over the fine line of nulls, computing the p-value of a post-intervention window of size 1, which starts at the period of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "61d7aa74",
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_val_grid(df, state, nulls, intervention_start, period, model):\n",
    "    \n",
    "    df_aug = df[df.index < intervention_start].append(df.loc[period])\n",
    "    \n",
    "    p_vals =  {null: p_value(residuals(df_aug,\n",
    "                                       state,\n",
    "                                       null=null,\n",
    "                                       intervention_start=period,\n",
    "                                       window=1,\n",
    "                                       model=model)) for null in nulls}        \n",
    "        \n",
    "    return pd.DataFrame(p_vals, index=[period]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "afb155ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1988</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-20.000000</th>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-19.595960</th>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-19.191919</th>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-18.787879</th>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-18.383838</th>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.383838</th>\n",
       "      <td>0.052632</td>\n",
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       "    <tr>\n",
       "      <th>18.787879</th>\n",
       "      <td>0.052632</td>\n",
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       "    <tr>\n",
       "      <th>19.191919</th>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.595960</th>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.000000</th>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1988\n",
       "-20.000000  0.052632\n",
       "-19.595960  0.052632\n",
       "-19.191919  0.052632\n",
       "-18.787879  0.052632\n",
       "-18.383838  0.052632\n",
       "...              ...\n",
       " 18.383838  0.052632\n",
       " 18.787879  0.052632\n",
       " 19.191919  0.052632\n",
       " 19.595960  0.052632\n",
       " 20.000000  0.052632\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SyntheticControl()\n",
    "\n",
    "nulls = np.linspace(-20, 20, 100)\n",
    "\n",
    "p_values_df = p_val_grid(\n",
    "    data,\n",
    "    \"california\",\n",
    "    nulls=nulls,\n",
    "    intervention_start=1988,\n",
    "    period=1988,\n",
    "    model=model\n",
    ")\n",
    "\n",
    "p_values_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde29c35",
   "metadata": {},
   "source": [
    "As you can see, the result is a table where the row index is the null hypothesis and the row values are the p-values.\n",
    " \n",
    "To build the confidence interval, all we need to do is filter out the $H_0$s that gave us a low P-value. Remember that low p-value means that the data we have is unlikely to have come from that null. For instance, if we define the significant level $\\alpha$ to be 0.1, we remove $H_0$s that have P-value lower than 0.1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "99ebbd8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def confidence_interval_from_p_values(p_values, alpha=0.1):\n",
    "    big_p_values = p_values[p_values.values >= alpha]\n",
    "    return pd.DataFrame({\n",
    "        f\"{int(100-alpha*100)}_ci_lower\": big_p_values.index.min(),\n",
    "        f\"{int(100-alpha*100)}_ci_upper\": big_p_values.index.max(),\n",
    "    }, index=[p_values.columns[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "86344ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>90_ci_lower</th>\n",
       "      <th>90_ci_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>-12.323232</td>\n",
       "      <td>8.686869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      90_ci_lower  90_ci_upper\n",
       "1988   -12.323232     8.686869"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confidence_interval_from_p_values(p_values_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b01a91b7",
   "metadata": {},
   "source": [
    "This gives us the confidence interval for the effect in 1988.\n",
    " \n",
    "We can also plot the $H_0$ by P-value to better understand how this confidence interval was obtained. In the figure below, the dashed line is the 0.1 line, which is the $\\alpha$ we've specified. The blue lines mark the confidence intervals. $H_0$ outside these lines have a P-value lower than 0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9fa3828f",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(p_values_df[1988], p_values_df.index)\n",
    "plt.xlabel(\"P-Value\")\n",
    "plt.ylabel(\"H0\")\n",
    "plt.vlines(0.1, nulls.min(), nulls.max(), color=\"black\", ls=\"dotted\", label=\"0.1\")\n",
    "\n",
    "plt.hlines(confidence_interval_from_p_values(p_values_df)[\"90_ci_upper\"], 0, 1, color=\"C1\", ls=\"dashed\")\n",
    "plt.hlines(confidence_interval_from_p_values(p_values_df)[\"90_ci_lower\"], 0, 1, color=\"C1\", ls=\"dashed\", label=\"90% CI\")\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"Confidence Interval for the Effect in 1988\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1bc9b0",
   "metadata": {},
   "source": [
    "All there's left to do is repeat the procedure above for each time period. This means that, for each post intervention year, appending it to the end of the pre-intervention period to create the augmented dataset and then computing the confidence interval just like we've done above.\n",
    " \n",
    "![img](data/img/sc-conformal-inf/aug-data.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "898f67fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_period_ci(df, state, nulls, intervention_start, period, model, alpha=0.1):\n",
    "    p_vals = p_val_grid(df=df,\n",
    "                        state=state,\n",
    "                        nulls=nulls,\n",
    "                        intervention_start=intervention_start,\n",
    "                        period=period,\n",
    "                        model=model)\n",
    "    \n",
    "    return confidence_interval_from_p_values(p_vals, alpha=alpha)\n",
    "\n",
    "\n",
    "def confidence_interval(df, state, nulls, intervention_start, window, model, alpha=0.1, jobs=4):    \n",
    "    return pd.concat([compute_period_ci(df, state, nulls, intervention_start, period, model, alpha)\n",
    "                     for period in range(intervention_start, intervention_start+window)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8698324",
   "metadata": {},
   "source": [
    "We are now ready to compute the confidence interval for all the post-intervention periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c004990e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>90_ci_lower</th>\n",
       "      <th>90_ci_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>-12.323232</td>\n",
       "      <td>8.686869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>-16.363636</td>\n",
       "      <td>2.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>-17.171717</td>\n",
       "      <td>5.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>-19.595960</td>\n",
       "      <td>-5.858586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>-22.828283</td>\n",
       "      <td>-7.474747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>-32.525253</td>\n",
       "      <td>-12.323232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>-36.565657</td>\n",
       "      <td>-15.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>-43.030303</td>\n",
       "      <td>-14.747475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>-41.414141</td>\n",
       "      <td>-16.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-48.686869</td>\n",
       "      <td>-13.131313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-46.262626</td>\n",
       "      <td>-13.939394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>-46.262626</td>\n",
       "      <td>-16.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>-51.111111</td>\n",
       "      <td>-17.979798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      90_ci_lower  90_ci_upper\n",
       "1988   -12.323232     8.686869\n",
       "1989   -16.363636     2.222222\n",
       "1990   -17.171717     5.454545\n",
       "1991   -19.595960    -5.858586\n",
       "1992   -22.828283    -7.474747\n",
       "1993   -32.525253   -12.323232\n",
       "1994   -36.565657   -15.555556\n",
       "1995   -43.030303   -14.747475\n",
       "1996   -41.414141   -16.363636\n",
       "1997   -48.686869   -13.131313\n",
       "1998   -46.262626   -13.939394\n",
       "1999   -46.262626   -16.363636\n",
       "2000   -51.111111   -17.979798"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SyntheticControl()\n",
    "\n",
    "nulls = np.linspace(-60, 20, 100)\n",
    "\n",
    "ci_df = confidence_interval(\n",
    "    data,\n",
    "    \"california\",\n",
    "    nulls=nulls,\n",
    "    intervention_start=1988,\n",
    "    window=2000 - 1988 + 1,\n",
    "    model=model\n",
    ")\n",
    "\n",
    "ci_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "13486f48",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.fill_between(ci_df.index, ci_df[\"90_ci_lower\"], ci_df[\"90_ci_upper\"], alpha=0.2,  color=\"C1\")\n",
    "plt.plot(pred_data[\"california\"].index, pred_data[\"residuals\"], label=\"California\", color=\"C1\")\n",
    "plt.hlines(y=0, xmin=1970, xmax=2000, lw=2, color=\"Black\")\n",
    "plt.vlines(x=1988, ymin=10, ymax=-50, linestyle=\":\", color=\"Black\", lw=2, label=\"Proposition 99\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Gap in per-capita cigarette sales (in packs)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7bb97e",
   "metadata": {},
   "source": [
    "## Reference\n",
    " \n",
    "This Appendix based on the paper *An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls*, by Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu. I would like to give special thanks to Kaspar, who clarified a lot of the questions I had. \n",
    " \n",
    "For additional resources on Synthetic Controls, check out *Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects*, by Alberto Abadie (2021). \n",
    " \n",
    "## Contribute\n",
    " \n",
    "Causal Inference for the Brave and True is an open-source material on causal inference, the statistics of science. It uses only free software, based in Python. Its goal is to be accessible monetarily and intellectually.\n",
    "If you found this book valuable and you want to support it, please go to [Patreon](https://www.patreon.com/causal_inference_for_the_brave_and_true). If you are not ready to contribute financially, you can also help by fixing typos, suggesting edits or giving feedback on passages you didn't understand. Just go to the book's repository and [open an issue](https://github.com/matheusfacure/python-causality-handbook/issues). Finally, if you liked this content, please share it with others who might find it useful and give it a [star on GitHub](https://github.com/matheusfacure/python-causality-handbook/stargazers)."
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